Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Knowledge Flow: Improve Upon Your Teachers
Authors: Iou-Jen Liu, Jian Peng, Alexander Schwing
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate knowledge flow on a variety of tasks from reinforcement learning to fully-supervised learning. |
| Researcher Affiliation | Academia | University of Illinois at Urbana-Champaign |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of its source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate knowledge flow on reinforcement learning using Atari games that were used by Rusu et al. (2016b); Fernando et al. (2017). For supervised learning, we use a variety of image classification benchmarks, including CIFAR10 (Krizhevsky, 2009), CIFAR-100 (Krizhevsky, 2009), STL-10 (Coates et al., 2011), and EMNIST (Cohen et al., 2017). |
| Dataset Splits | Yes | The parameters λ1 for the dependent cost and λ2 for the KL cost are determined using the validation set of each dataset. |
| Hardware Specification | No | As A3C, we run 16 agents on 16 CPU cores in parallel. (This is not specific enough to meet the criteria for "Yes") |
| Software Dependencies | No | The paper does not provide specific software names with version numbers for replication. |
| Experiment Setup | Yes | The learning rate is set to 10 4 and gradually decreased to zero for all experiments. To select λ1 and λ2 in our framework, we follow progressive neural net (Rusu et al., 2016b): randomly sample λ1 {0.05, 0.1, 0.5} and λ2 {0.001, 0.01, 0.05}. |